Climate-conscious inhaler prescribing for family physicians
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with prescribing and diagnostic strategies that can reduce carbon emissions associated with inhalers. SOURCES OF INFORMATION: This review is based on the authors' experience developing the climate-conscious inhaler prescribing playbooks and courses for CASCADES (Creating a Sustainable Canadian Health System in a Climate Crisis). The approach was refined through patient and provider feedback since the first playbook was published in 2021. PubMed was also searched for relevant publications on inhaler use, asthma management, and chronic obstructive pulmonary disease (COPD) management. Current asthma and COPD guidelines were also reviewed. MAIN MESSAGE: There is growing acknowledgment of the substantial impact that inhalers have on climate emissions generated by the health sector. Recent surveys indicate that most Canadian patients care about climate change and would be willing to opt for less carbon-intensive treatment and care delivery options where available. Beyond inhaler choice, there are many opportunities to address the climate impacts of respiratory care and enhance quality of care. Working with patients to ensure they are using the right medications in the right ways will produce both carbon savings and better health outcomes. The climate crisis can therefore serve as a catalyst for improving treatment of patients with respiratory conditions. Family physicians may reduce carbon emissions associated with inhalers by reducing unnecessary inhaler prescribing; ensuring patients' control of asthma and COPD is optimized; considering whether a more sustainable inhaler may be appropriate; optimizing dosing technique to reduce emissions and waste; and disposing of inhalers appropriately if possible. CONCLUSION: Family physicians may reduce carbon emissions associated with inhalers through the following strategies: confirming diagnosis, controlling disease, considering inhaler type, optimizing dosing technique, and encouraging appropriate disposal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".